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Ggplot2 is usually focused on static plots, but gganimate extends the API and grammar to describe animations. As such it feels like a very natural extension of using ggplot2

Animation - optional

echarts4r is a better option for animation.

Examples using echarts4r follow those using gganimate

ggplot(economics) + 
  geom_line(aes(x = date, y = unemploy))

ggplot(economics) + 
  geom_line(aes(x = date, y = unemploy)) + 
  transition_reveal(along = date)

Using echarts4r

economics  %>% 
  e_charts(x = date)  %>% 
  e_line(serie = unemploy)
economics  %>%
  e_charts(x = date)  %>% 
  e_line(serie = unemploy)  %>% 
  e_animation(duration = 8000)
economics  %>%
  e_charts(x = date)  %>% 
  e_line(serie = unemploy)  %>% 
  e_line(serie = pop)  %>% 
  e_animation(duration = 8000)

There are many different transitions that control how data is interpreted for animation, as well as a range of other animation specific features

ggplot(mpg) + 
  geom_bar(aes(x = factor(cyl)))

ggplot(mpg) + 
  geom_bar(aes(x = factor(cyl))) + 
  labs(title = 'Number of cars in {closest_state} by number of cylinders') + 
  transition_states(states = year) + 
  enter_grow() + 
  exit_fade()

Using echarts4r

  • group_by the variable you want animate

  • Set timeline = TRUE for an animation

  • Modify an animation with e_animation.

  • See the article on the timeline component

mpg  %>%
  e_charts(x = cyl)  %>% 
  e_bar(cyl)
mpg  %>% 
  group_by(year)  %>% 
  e_charts(x= cyl, timeline = TRUE)  %>% 
  e_timeline_opts(autoPlay = TRUE) %>% 
  e_bar(cyl)  %>% 
  e_timeline_serie(
    title = list(
      list(text = "1999", subtext = "Number of cars by number of cylinders"),
      list(text = "2008", subtext = "Number of cars by number of cylinders")
    )
    ) %>% 
  e_legend(FALSE)

Exercises animation

The animation below will animate between points showing cars with different cylinders.

ggplot(mpg) + 
  geom_point(aes(x = displ, y = hwy)) + 
  ggtitle("Cars with {closest_state} cylinders") + 
  transition_states(factor(cyl))

gganimate uses the group aesthetic to match observations between states. By default the group aesthetic is set to the same value, so observations are matched by their position (first row of 4 cyl is matched to first row of 5 cyl etc.). This is clearly wrong here (why?). Add a mapping to the group aesthetic to ensure that points do not move between the different states.

In the presence of discrete aesthetic mappings (colour below), the group is deduced if not given. The default behaviour of objects that appear and disappear during the animation is to simply pop in and out of existance. enter_*() and exit_*() functions can be used to control this behaviour. Experiment with the different enter and exit functions provided by gganimate below. What happens if you add multiple enter or exit functions to the same animation?

ggplot(mpg) + 
  geom_point(aes(x = displ, y = hwy, colour = factor(cyl))) + 
  ggtitle("Cars with {closest_state} cylinders") + 
  transition_states(factor(cyl))

In the animation below (as in all the other animations) the changes happens at constant speed. How values change during an animation is called easing and can be controlled using the ease_aes() function. Read the documentation for ease_aes() and experiment with different easings in the animation.

# check out package tidyr (part of tidyverse) for tidying data: https://tidyr.tidyverse.org

mpg2 <- tidyr::pivot_longer(mpg, c(cty,hwy))
ggplot(mpg2) + 
  geom_point(aes(x = displ, y = value)) + 
  ggtitle("{if (closest_state == 'cty') 'Efficiency in city' else 'Efficiency on highway'}") + 
  transition_states(name)

Annotation

Text is a huge part of storytelling with your visualisation. Historically, textual annotations has not been the best part of ggplot2 but new extensions make up for that.

Standard geom_text will often result in overlaping labels

ggplot(mtcars, aes(x = disp, y = mpg)) + 
  geom_point() + 
  geom_text(aes(label = row.names(mtcars)))

ggrepel takes care of that

ggplot(mtcars, aes(x = disp, y = mpg)) + 
  geom_point() + 
  geom_text_repel(aes(label = row.names(mtcars)))

If you want to highlight certain parts of your data and describe it, the geom_mark_*() family of geoms have your back

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point() + 
  geom_mark_ellipse(aes(filter = gear == 4,
                        label = '4 gear cars',
                        description = 'Cars with fewer gears tend to both have higher yield and lower displacement'))

Exercises - annotation

ggrepel has a tonne of settings for controlling how text labels move. Often, though, the most effective is simply to not label everything. There are two strategies for that: Either only use a subset of the data for the repel layer, or setting the label to "" for those you don’t want to plot. Try both in the plot below where you only label 10 random points.

mtcars2 <- mtcars
mtcars2$label <- rownames(mtcars2)
points_to_label <- sample(nrow(mtcars), 10)
ggplot(mtcars2, aes(x = disp, y = mpg)) + 
  geom_point() + 
  geom_text_repel(aes(label = points_to_label))

Explore the documentation for geom_text_repel. Find a way to ensure that the labels in the plot below only repels in the vertical direction

mtcars2$label <- ""
mtcars2$label[1:10] <- rownames(mtcars2)[1:10]
ggplot(mtcars2, aes(x = disp, y = mpg)) + 
  geom_point() + 
  geom_text_repel(aes(label = label))

ggforce comes with 4 different types of mark geoms. Try them all out in the code below:

ggplot(mtcars, aes(x = disp, y = mpg)) +
  geom_point() + 
  geom_mark_ellipse(aes(filter = gear == 4,
                        label = '4 gear cars'))

Networks

ggplot2 has been focused on tabular data. Network data in any shape and form is handled by ggraph

graph <- create_notable('zachary') %>% 
  mutate(clique = as.factor(group_infomap()))

ggraph(graph) + 
  geom_mark_hull(aes(x, y, fill = clique)) + 
  geom_edge_link() + 
  geom_node_point(size = 2)

dendrograms are just a specific type of network

iris_clust <- hclust(dist(iris[, 1:4]))
ggraph(iris_clust) + 
  geom_edge_bend() + 
  geom_node_point(aes(filter = leaf))

Exercies- networks

Most network plots are defined by a layout algorithm, which takes the network structure and calculate a position for each node. The layout algorithm is global and set in the ggraph(). The default auto layout will inspect the network object and try to choose a sensible layout for it (e.g. dendrogram for a hierarchical clustering as above). There is, however no optimal layout and it is often a good idea to try out different layouts. Try out different layouts in the graph below. See the the website for an overview of the different layouts.

ggraph(graph) + 
  geom_edge_link() + 
  geom_node_point(aes(colour = clique), size = 3)

There are many different ways to draw edges. Try to use geom_edge_parallel() in the graph below to show the presence of multiple edges

highschool_gr <- as_tbl_graph(highschool)
ggraph(highschool_gr) + 
  geom_edge_link() + 
  geom_node_point()

Faceting works in ggraph as it does in ggplot2, but you must choose to facet by either nodes or edges. Modify the graph below to facet the edges by the year variable (using facet_edges())

ggraph(highschool_gr) + 
  geom_edge_fan() + 
  geom_node_point()

Looks

Many people have already designed beautiful (and horrible) themes for you. Use them as a base

p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point(aes(color = factor(carb))) +
  labs(
    x = 'Fuel efficiency (mpg)', 
    y = 'Weight (tons)',
    title = 'Seminal ggplot2 example',
    subtitle = 'A plot to show off different themes',
    caption = 'Source: It’s mtcars — everyone uses it'
  )

p + 
  scale_colour_ipsum() + 
  theme_ipsum()

# library(ggthemes)
p + 
  scale_colour_excel() + 
  theme_excel()

Drawing anything

states <- c(
  'eaten', "eaten but said you didn\'t", 'cat took it', 'for tonight',
  'will decompose slowly'
)
pie <- data.frame(
  state = factor(states, levels = states),
  amount = c(4, 3, 1, 1.5, 6),
  stringsAsFactors = FALSE
)
ggplot(pie) + 
  geom_col(aes(x = 0, y = amount, fill = state))

ggplot(pie) + 
  geom_col(aes(x = 0, y = amount, fill = state)) + 
  coord_polar(theta = 'y')

ggplot(pie) + 
  geom_col(aes(x = 0, y = amount, fill = state)) + 
  coord_polar(theta = 'y') + 
  scale_fill_tableau(name = NULL,
                     guide = guide_legend(ncol = 2)) + 
  theme_void() + 
  theme(legend.position = 'top', 
        legend.justification = 'left')

ggplot(pie) + 
  geom_arc_bar(aes(x0 = 0, y0 = 0, r0 = 0, r = 1, amount = amount, fill = state), stat = 'pie') + 
  coord_fixed()

ggplot(pie) + 
  geom_arc_bar(aes(x0 = 0, y0 = 0, r0 = 0, r = 1, amount = amount, fill = state), stat = 'pie') + 
  coord_fixed() + 
  scale_fill_tableau(name = NULL,
                     guide = guide_legend(ncol = 2)) + 
  theme_void() + 
  theme(legend.position = 'top', 
        legend.justification = 'left')

ggplot(mpg) + 
  # geom_bar(aes(x = hwy), stat = 'bin')
  geom_histogram(aes(x = hwy))

ggplot(mpg) + 
  geom_bar(aes(x = hwy)) + 
  scale_x_binned(n.breaks = 30, guide = guide_axis(n.dodge = 2))